Abstract
An information geometrical method is developed for characterizing or classifying neurons in cortical areas, whose spike rates fluctuate in time. When the interspike intervals of a spike sequence of a neuron obey a gamma process with a time-variant spike rate and a fixed shape parameter, the information geometry for semiparametric estimation has given the optimal method from the statistical viewpoint. Recently a more suitable statistical model for interspike intervals is proposed, which have an absolute refractory period. This work extends the information geometrical method and derives the optimal method for the new model.
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Komazawa, D., Ikeda, K., Funaya, H. (2009). Information Geometry of Interspike Intervals in Spiking Neurons with Refractories. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_89
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DOI: https://doi.org/10.1007/978-3-642-02490-0_89
Publisher Name: Springer, Berlin, Heidelberg
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